Towards Distributed Pairwise Ranking using Implicit Feedback

Mohsan Jameel, Nicolas Schilling, Lars Schmidt-Thieme

Abstract: Learning with pairwise ranking methods for implicit feedback datasets has shown promising results as compared to pointwise ranking methods for recommendation tasks. However, there is limited effort in scaling the pairwise ranking methods in a large scale distributed setting. In this paper we address the scalability aspect of a pairwise ranking method using Factorization Machines in distributed settings. Our proposed method is based on a block partitioning of the model parameters so that each distributed worker runs stochastic gradient updates on an independent block. We developed a dynamic block creation and exchange strategy by utilizing the frequency of occurrence of a feature in the local training data of a worker. Empirical evidence on publicly available benchmark datasets indicates that the proposed method scales better than the static block based methods and outperforms competing state-of-the-art methods.